--- title: 1. Challenge: dolphin instance segmentation model keywords: fastai sidebar: home_sidebar summary: "The goal of this challenge is to find all instances of dolphins in a picture and then color pixes of each dolphin with a unique color." description: "The goal of this challenge is to find all instances of dolphins in a picture and then color pixes of each dolphin with a unique color." nb_path: "notebooks/01_Dolphin_instance_segmentation_challenge.ipynb" ---
numpy : 1.18.5 torch : 1.7.1 torchvision : 0.8.2 PIL : 7.2.0
We start by downloading and visualizing the dataset containing 200 photographs with one or more dolphins split into a training set containing 160 photographs and a validation set containing 40 photographs.
from dolphins_recognition_challenge.datasets import get_dataset, display_batches
data_loader, data_loader_test = get_dataset("segmentation", batch_size=3)
display_batches(data_loader, n_batches=2, width=600)
In order to prevent overfitting which happens when the dataset size is too small, we perform a number of transformations to increase the size of the dataset. One transofrmation implemented in the Torch vision library is RandomHorizontalFlip and we will implemented MyColorJitter which is basically just a wrapper around torchvision.transforms.ColorJitter class. However, we cannot use this class directly without a wrapper because a transofrmation could possibly affect targets and not just the image. For example, if we were to implement RandomCrop, we would need to crop segmentation masks and readjust bounding boxes as well.
class MyColorJitter:
def __init__(self, brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5):
self.torch_color_jitter = torchvision.transforms.ColorJitter(
brightness=brightness, contrast=contrast, saturation=saturation, hue=hue
)
def __call__(self, image, target):
image = self.torch_color_jitter(image)
return image, target
We will make a series of transformations on an image and we will combine all those transofrmations in a single one as follows:
import transforms as T
def get_tensor_transforms(train):
transforms = []
# converts the image, a PIL image, into a PyTorch Tensor
transforms.append(T.ToTensor())
if train:
# during training, randomly flip the training images
# and ground-truth for data augmentation
transforms.append(
MyColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5)
)
transforms.append(T.RandomHorizontalFlip(0.5))
# TODO: add additional transforms: e.g. random crop
return T.Compose(transforms)
data_loader, data_loader_test = get_dataset("segmentation", batch_size=2, get_tensor_transforms=get_tensor_transforms)
display_batches(data_loader, n_batches=2, width=800)
With data augementation defined, we are ready to generate the actual datasets used for training our models.
batch_size = 4
data_loader, data_loader_test = get_dataset(
"segmentation", get_tensor_transforms=get_tensor_transforms, batch_size=batch_size
)
display_batches(data_loader, n_batches=4, width=800)
{% include tip.html content='incorporate more transformation classes such as RandomCrop etc. (https://pytorch.org/docs/stable/torchvision/transforms.html)' %}
def get_instance_segmentation_model(hidden_layer_size):
# our dataset has two classes only - background and dolphin
num_classes = 2
# load an instance segmentation model pre-trained on COCO
model = torchvision.models.detection.maskrcnn_resnet50_fpn(
pretrained=True
) # box_score_thresh=0.5
# get the number of input features for the classifier
in_features = model.roi_heads.box_predictor.cls_score.in_features
# replace the pre-trained head with a new one
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
# now get the number of input features for the mask classifier
in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels
model.roi_heads.mask_predictor = MaskRCNNPredictor(
in_channels=in_features_mask,
dim_reduced=hidden_layer_size,
num_classes=num_classes
)
return model
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
# get the model using our helper function
model = get_instance_segmentation_model(hidden_layer_size=256)
# move model to the right device
model.to(device)
# construct an optimizer
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=0.005, momentum=0.9, weight_decay=0.0005)
# and a learning rate scheduler which decreases the learning rate by
# 10x every 3 epochs
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)
from traceback_with_variables import printing_tb
from engine import train_one_epoch, evaluate
# let's train it for 20 epochs
num_epochs = 20
print("Training...")
with printing_tb():
for epoch in range(num_epochs):
# train for one epoch, printing every 10 iterations
train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)
# update the learning rate
lr_scheduler.step()
# evaluate on the test dataset
evaluate(model, data_loader_test, device=device)
Training...
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:3103: UserWarning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and now uses scale_factor directly, instead of relying on the computed output size. If you wish to restore the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.
warnings.warn("The default behavior for interpolate/upsample with float scale_factor changed "
Epoch: [0] [ 0/40] eta: 0:00:51 lr: 0.000133 loss: 3.8964 (3.8964) loss_classifier: 0.9773 (0.9773) loss_box_reg: 0.2797 (0.2797) loss_mask: 2.5986 (2.5986) loss_objectness: 0.0113 (0.0113) loss_rpn_box_reg: 0.0295 (0.0295) time: 1.2918 data: 0.7365 max mem: 4477 Epoch: [0] [10/40] eta: 0:00:16 lr: 0.001414 loss: 1.3453 (2.1033) loss_classifier: 0.2631 (0.4807) loss_box_reg: 0.2872 (0.2843) loss_mask: 0.7485 (1.2939) loss_objectness: 0.0135 (0.0278) loss_rpn_box_reg: 0.0095 (0.0166) time: 0.5513 data: 0.0742 max mem: 5189 Epoch: [0] [20/40] eta: 0:00:10 lr: 0.002695 loss: 1.0537 (1.5074) loss_classifier: 0.2357 (0.3438) loss_box_reg: 0.2662 (0.2664) loss_mask: 0.4620 (0.8403) loss_objectness: 0.0258 (0.0356) loss_rpn_box_reg: 0.0098 (0.0215) time: 0.4777 data: 0.0080 max mem: 5189 Epoch: [0] [30/40] eta: 0:00:05 lr: 0.003975 loss: 0.6962 (1.2475) loss_classifier: 0.1226 (0.2692) loss_box_reg: 0.2354 (0.2580) loss_mask: 0.2537 (0.6471) loss_objectness: 0.0258 (0.0364) loss_rpn_box_reg: 0.0248 (0.0368) time: 0.4791 data: 0.0082 max mem: 5189 Epoch: [0] [39/40] eta: 0:00:00 lr: 0.005000 loss: 0.6535 (1.1129) loss_classifier: 0.1009 (0.2299) loss_box_reg: 0.2537 (0.2589) loss_mask: 0.2390 (0.5548) loss_objectness: 0.0162 (0.0333) loss_rpn_box_reg: 0.0138 (0.0359) time: 0.4851 data: 0.0084 max mem: 5189 Epoch: [0] Total time: 0:00:20 (0.5036 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:10 model_time: 0.3962 (0.3962) evaluator_time: 0.1608 (0.1608) time: 1.0478 data: 0.4880 max mem: 5189 Test: [ 9/10] eta: 0:00:00 model_time: 0.3123 (0.3242) evaluator_time: 0.1065 (0.1107) time: 0.4971 data: 0.0542 max mem: 5189 Test: Total time: 0:00:05 (0.5032 s / it) Averaged stats: model_time: 0.3123 (0.3242) evaluator_time: 0.1065 (0.1107) Accumulating evaluation results... DONE (t=0.02s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.308 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.692 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.213 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.194 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.294 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.471 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.152 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.428 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.470 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.416 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.467 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.530 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.331 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.699 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.266 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.143 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.328 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.517 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.152 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.466 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.518 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.479 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.501 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.600 Epoch: [1] [ 0/40] eta: 0:00:49 lr: 0.005000 loss: 0.7875 (0.7875) loss_classifier: 0.1042 (0.1042) loss_box_reg: 0.2977 (0.2977) loss_mask: 0.3597 (0.3597) loss_objectness: 0.0119 (0.0119) loss_rpn_box_reg: 0.0139 (0.0139) time: 1.2469 data: 0.7677 max mem: 5189 Epoch: [1] [10/40] eta: 0:00:16 lr: 0.005000 loss: 0.6108 (0.5808) loss_classifier: 0.0833 (0.0781) loss_box_reg: 0.2088 (0.2200) loss_mask: 0.2108 (0.2290) loss_objectness: 0.0067 (0.0088) loss_rpn_box_reg: 0.0117 (0.0450) time: 0.5543 data: 0.0756 max mem: 5189 Epoch: [1] [20/40] eta: 0:00:10 lr: 0.005000 loss: 0.6018 (0.6000) loss_classifier: 0.0833 (0.0915) loss_box_reg: 0.2180 (0.2223) loss_mask: 0.2108 (0.2391) loss_objectness: 0.0062 (0.0103) loss_rpn_box_reg: 0.0126 (0.0369) time: 0.5115 data: 0.0070 max mem: 5189 Epoch: [1] [30/40] eta: 0:00:05 lr: 0.005000 loss: 0.5341 (0.5606) loss_classifier: 0.0825 (0.0877) loss_box_reg: 0.1813 (0.2030) loss_mask: 0.1987 (0.2265) loss_objectness: 0.0084 (0.0106) loss_rpn_box_reg: 0.0160 (0.0328) time: 0.5386 data: 0.0076 max mem: 5189 Epoch: [1] [39/40] eta: 0:00:00 lr: 0.005000 loss: 0.4563 (0.5389) loss_classifier: 0.0737 (0.0846) loss_box_reg: 0.1535 (0.1935) loss_mask: 0.1899 (0.2218) loss_objectness: 0.0063 (0.0107) loss_rpn_box_reg: 0.0123 (0.0283) time: 0.5404 data: 0.0078 max mem: 5189 Epoch: [1] Total time: 0:00:21 (0.5454 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:08 model_time: 0.2817 (0.2817) evaluator_time: 0.0577 (0.0577) time: 0.8407 data: 0.4985 max mem: 5189 Test: [ 9/10] eta: 0:00:00 model_time: 0.2295 (0.2343) evaluator_time: 0.0434 (0.0458) time: 0.3379 data: 0.0546 max mem: 5189 Test: Total time: 0:00:03 (0.3441 s / it) Averaged stats: model_time: 0.2295 (0.2343) evaluator_time: 0.0434 (0.0458) Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.411 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.803 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.360 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.261 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.383 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.631 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.184 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.516 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.531 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.437 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.512 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.683 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.419 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.756 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.420 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.229 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.405 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.651 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.189 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.514 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.534 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.463 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.513 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.657 Epoch: [2] [ 0/40] eta: 0:00:47 lr: 0.005000 loss: 0.4882 (0.4882) loss_classifier: 0.0790 (0.0790) loss_box_reg: 0.1624 (0.1624) loss_mask: 0.2121 (0.2121) loss_objectness: 0.0085 (0.0085) loss_rpn_box_reg: 0.0262 (0.0262) time: 1.1912 data: 0.7036 max mem: 5189 Epoch: [2] [10/40] eta: 0:00:17 lr: 0.005000 loss: 0.5040 (0.4761) loss_classifier: 0.0683 (0.0731) loss_box_reg: 0.1348 (0.1604) loss_mask: 0.2029 (0.1887) loss_objectness: 0.0040 (0.0117) loss_rpn_box_reg: 0.0113 (0.0423) time: 0.5815 data: 0.0727 max mem: 5189 Epoch: [2] [20/40] eta: 0:00:11 lr: 0.005000 loss: 0.4084 (0.4441) loss_classifier: 0.0575 (0.0700) loss_box_reg: 0.1345 (0.1506) loss_mask: 0.1856 (0.1862) loss_objectness: 0.0044 (0.0092) loss_rpn_box_reg: 0.0075 (0.0280) time: 0.5348 data: 0.0085 max mem: 5189 Epoch: [2] [30/40] eta: 0:00:05 lr: 0.005000 loss: 0.4422 (0.4608) loss_classifier: 0.0809 (0.0759) loss_box_reg: 0.1508 (0.1583) loss_mask: 0.1871 (0.1896) loss_objectness: 0.0069 (0.0104) loss_rpn_box_reg: 0.0108 (0.0265) time: 0.5550 data: 0.0071 max mem: 5189 Epoch: [2] [39/40] eta: 0:00:00 lr: 0.005000 loss: 0.4422 (0.4476) loss_classifier: 0.0846 (0.0742) loss_box_reg: 0.1623 (0.1573) loss_mask: 0.1801 (0.1841) loss_objectness: 0.0056 (0.0091) loss_rpn_box_reg: 0.0105 (0.0228) time: 0.5611 data: 0.0068 max mem: 5189 Epoch: [2] Total time: 0:00:22 (0.5657 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:08 model_time: 0.2534 (0.2534) evaluator_time: 0.0418 (0.0418) time: 0.8188 data: 0.5209 max mem: 5189 Test: [ 9/10] eta: 0:00:00 model_time: 0.2125 (0.2198) evaluator_time: 0.0315 (0.0325) time: 0.3121 data: 0.0570 max mem: 5189 Test: Total time: 0:00:03 (0.3177 s / it) Averaged stats: model_time: 0.2125 (0.2198) evaluator_time: 0.0315 (0.0325) Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.429 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.804 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.458 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.280 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.428 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.565 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.203 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.528 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.529 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.442 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.519 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.652 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.397 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.797 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.360 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.226 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.383 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.564 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.178 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.496 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.503 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.516 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.463 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.617 Epoch: [3] [ 0/40] eta: 0:00:51 lr: 0.005000 loss: 0.2576 (0.2576) loss_classifier: 0.0448 (0.0448) loss_box_reg: 0.0763 (0.0763) loss_mask: 0.1327 (0.1327) loss_objectness: 0.0014 (0.0014) loss_rpn_box_reg: 0.0024 (0.0024) time: 1.2926 data: 0.7692 max mem: 5189 Epoch: [3] [10/40] eta: 0:00:17 lr: 0.005000 loss: 0.3994 (0.3798) loss_classifier: 0.0553 (0.0600) loss_box_reg: 0.1143 (0.1225) loss_mask: 0.1784 (0.1831) loss_objectness: 0.0033 (0.0035) loss_rpn_box_reg: 0.0077 (0.0107) time: 0.5933 data: 0.0748 max mem: 5189 Epoch: [3] [20/40] eta: 0:00:11 lr: 0.005000 loss: 0.4097 (0.4063) loss_classifier: 0.0623 (0.0629) loss_box_reg: 0.1350 (0.1319) loss_mask: 0.1610 (0.1762) loss_objectness: 0.0035 (0.0042) loss_rpn_box_reg: 0.0097 (0.0312) time: 0.5446 data: 0.0064 max mem: 5189 Epoch: [3] [30/40] eta: 0:00:05 lr: 0.005000 loss: 0.4097 (0.4061) loss_classifier: 0.0673 (0.0658) loss_box_reg: 0.1386 (0.1350) loss_mask: 0.1526 (0.1738) loss_objectness: 0.0045 (0.0049) loss_rpn_box_reg: 0.0132 (0.0265) time: 0.5699 data: 0.0072 max mem: 5189 Epoch: [3] [39/40] eta: 0:00:00 lr: 0.005000 loss: 0.3495 (0.3914) loss_classifier: 0.0637 (0.0642) loss_box_reg: 0.1208 (0.1306) loss_mask: 0.1485 (0.1694) loss_objectness: 0.0042 (0.0046) loss_rpn_box_reg: 0.0083 (0.0227) time: 0.5727 data: 0.0070 max mem: 5189 Epoch: [3] Total time: 0:00:23 (0.5783 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:08 model_time: 0.2548 (0.2548) evaluator_time: 0.0451 (0.0451) time: 0.8060 data: 0.5032 max mem: 5189 Test: [ 9/10] eta: 0:00:00 model_time: 0.2177 (0.2221) evaluator_time: 0.0369 (0.0376) time: 0.3174 data: 0.0549 max mem: 5189 Test: Total time: 0:00:03 (0.3230 s / it) Averaged stats: model_time: 0.2177 (0.2221) evaluator_time: 0.0369 (0.0376) Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.470 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.845 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.469 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.314 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.476 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.626 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.228 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.562 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.567 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.458 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.564 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.696 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.452 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.814 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.433 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.207 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.457 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.604 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.222 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.534 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.542 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.463 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.531 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.639 Epoch: [4] [ 0/40] eta: 0:00:49 lr: 0.005000 loss: 0.2011 (0.2011) loss_classifier: 0.0350 (0.0350) loss_box_reg: 0.0710 (0.0710) loss_mask: 0.0920 (0.0920) loss_objectness: 0.0011 (0.0011) loss_rpn_box_reg: 0.0020 (0.0020) time: 1.2265 data: 0.7473 max mem: 5189 Epoch: [4] [10/40] eta: 0:00:17 lr: 0.005000 loss: 0.3274 (0.3180) loss_classifier: 0.0487 (0.0498) loss_box_reg: 0.1070 (0.1064) loss_mask: 0.1502 (0.1500) loss_objectness: 0.0013 (0.0025) loss_rpn_box_reg: 0.0065 (0.0092) time: 0.5985 data: 0.0733 max mem: 5189 Epoch: [4] [20/40] eta: 0:00:11 lr: 0.005000 loss: 0.3445 (0.3306) loss_classifier: 0.0537 (0.0540) loss_box_reg: 0.1130 (0.1119) loss_mask: 0.1531 (0.1532) loss_objectness: 0.0018 (0.0027) loss_rpn_box_reg: 0.0067 (0.0088) time: 0.5579 data: 0.0068 max mem: 5189 Epoch: [4] [30/40] eta: 0:00:05 lr: 0.005000 loss: 0.3464 (0.3420) loss_classifier: 0.0531 (0.0527) loss_box_reg: 0.1100 (0.1104) loss_mask: 0.1475 (0.1528) loss_objectness: 0.0020 (0.0029) loss_rpn_box_reg: 0.0087 (0.0232) time: 0.5791 data: 0.0080 max mem: 5189 Epoch: [4] [39/40] eta: 0:00:00 lr: 0.005000 loss: 0.3464 (0.3525) loss_classifier: 0.0532 (0.0554) loss_box_reg: 0.1089 (0.1143) loss_mask: 0.1521 (0.1550) loss_objectness: 0.0038 (0.0042) loss_rpn_box_reg: 0.0119 (0.0236) time: 0.5813 data: 0.0081 max mem: 5189 Epoch: [4] Total time: 0:00:23 (0.5878 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:08 model_time: 0.2599 (0.2599) evaluator_time: 0.0443 (0.0443) time: 0.8043 data: 0.4971 max mem: 5189 Test: [ 9/10] eta: 0:00:00 model_time: 0.2221 (0.2264) evaluator_time: 0.0369 (0.0378) time: 0.3219 data: 0.0545 max mem: 5189 Test: Total time: 0:00:03 (0.3277 s / it) Averaged stats: model_time: 0.2221 (0.2264) evaluator_time: 0.0369 (0.0378) Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.498 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.861 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.533 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.270 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.500 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.685 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.238 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.583 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.595 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.426 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.605 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.717 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.451 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.826 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.452 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.217 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.450 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.648 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.215 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.541 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.556 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.458 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.537 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.691 Epoch: [5] [ 0/40] eta: 0:00:47 lr: 0.005000 loss: 0.2295 (0.2295) loss_classifier: 0.0628 (0.0628) loss_box_reg: 0.0571 (0.0571) loss_mask: 0.1022 (0.1022) loss_objectness: 0.0047 (0.0047) loss_rpn_box_reg: 0.0027 (0.0027) time: 1.1821 data: 0.6976 max mem: 5189 Epoch: [5] [10/40] eta: 0:00:18 lr: 0.005000 loss: 0.2750 (0.3354) loss_classifier: 0.0467 (0.0482) loss_box_reg: 0.0926 (0.0966) loss_mask: 0.1430 (0.1420) loss_objectness: 0.0043 (0.0049) loss_rpn_box_reg: 0.0047 (0.0437) time: 0.6068 data: 0.0688 max mem: 5189 Epoch: [5] [20/40] eta: 0:00:11 lr: 0.005000 loss: 0.3444 (0.3535) loss_classifier: 0.0517 (0.0531) loss_box_reg: 0.1003 (0.1072) loss_mask: 0.1504 (0.1532) loss_objectness: 0.0043 (0.0049) loss_rpn_box_reg: 0.0054 (0.0351) time: 0.5674 data: 0.0065 max mem: 5189 Epoch: [5] [30/40] eta: 0:00:05 lr: 0.005000 loss: 0.3459 (0.3433) loss_classifier: 0.0538 (0.0516) loss_box_reg: 0.1261 (0.1073) loss_mask: 0.1531 (0.1527) loss_objectness: 0.0022 (0.0042) loss_rpn_box_reg: 0.0093 (0.0274) time: 0.5876 data: 0.0072 max mem: 5189 Epoch: [5] [39/40] eta: 0:00:00 lr: 0.005000 loss: 0.3459 (0.3465) loss_classifier: 0.0538 (0.0529) loss_box_reg: 0.1120 (0.1086) loss_mask: 0.1593 (0.1541) loss_objectness: 0.0022 (0.0047) loss_rpn_box_reg: 0.0081 (0.0262) time: 0.5885 data: 0.0072 max mem: 5189 Epoch: [5] Total time: 0:00:23 (0.5946 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:08 model_time: 0.2557 (0.2557) evaluator_time: 0.0388 (0.0388) time: 0.8047 data: 0.5073 max mem: 5189 Test: [ 9/10] eta: 0:00:00 model_time: 0.2154 (0.2201) evaluator_time: 0.0313 (0.0318) time: 0.3097 data: 0.0550 max mem: 5189 Test: Total time: 0:00:03 (0.3155 s / it) Averaged stats: model_time: 0.2154 (0.2201) evaluator_time: 0.0313 (0.0318) Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.507 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.867 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.523 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.245 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.505 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.703 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.237 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.596 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.600 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.463 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.593 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.748 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.461 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.827 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.450 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.196 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.461 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.630 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.209 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.557 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.565 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.505 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.552 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.648 Epoch: [6] [ 0/40] eta: 0:00:49 lr: 0.005000 loss: 0.2911 (0.2911) loss_classifier: 0.0398 (0.0398) loss_box_reg: 0.1061 (0.1061) loss_mask: 0.1406 (0.1406) loss_objectness: 0.0008 (0.0008) loss_rpn_box_reg: 0.0038 (0.0038) time: 1.2280 data: 0.7378 max mem: 5189 Epoch: [6] [10/40] eta: 0:00:18 lr: 0.005000 loss: 0.3162 (0.3111) loss_classifier: 0.0418 (0.0453) loss_box_reg: 0.1036 (0.1030) loss_mask: 0.1478 (0.1490) loss_objectness: 0.0030 (0.0035) loss_rpn_box_reg: 0.0087 (0.0104) time: 0.6205 data: 0.0742 max mem: 5189 Epoch: [6] [20/40] eta: 0:00:12 lr: 0.005000 loss: 0.3162 (0.3023) loss_classifier: 0.0405 (0.0431) loss_box_reg: 0.0959 (0.0984) loss_mask: 0.1447 (0.1466) loss_objectness: 0.0018 (0.0026) loss_rpn_box_reg: 0.0067 (0.0116) time: 0.5761 data: 0.0077 max mem: 5189 Epoch: [6] [30/40] eta: 0:00:06 lr: 0.005000 loss: 0.3261 (0.3177) loss_classifier: 0.0402 (0.0464) loss_box_reg: 0.0932 (0.1000) loss_mask: 0.1418 (0.1457) loss_objectness: 0.0017 (0.0050) loss_rpn_box_reg: 0.0092 (0.0205) time: 0.5942 data: 0.0074 max mem: 5189 Epoch: [6] [39/40] eta: 0:00:00 lr: 0.005000 loss: 0.3042 (0.3130) loss_classifier: 0.0426 (0.0456) loss_box_reg: 0.0963 (0.1011) loss_mask: 0.1211 (0.1439) loss_objectness: 0.0029 (0.0048) loss_rpn_box_reg: 0.0065 (0.0176) time: 0.5955 data: 0.0070 max mem: 5189 Epoch: [6] Total time: 0:00:24 (0.6034 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:07 model_time: 0.2533 (0.2533) evaluator_time: 0.0318 (0.0318) time: 0.7994 data: 0.5112 max mem: 5189 Test: [ 9/10] eta: 0:00:00 model_time: 0.2063 (0.2121) evaluator_time: 0.0219 (0.0236) time: 0.2945 data: 0.0560 max mem: 5189 Test: Total time: 0:00:03 (0.3013 s / it) Averaged stats: model_time: 0.2063 (0.2121) evaluator_time: 0.0219 (0.0236) Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.472 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.831 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.478 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.258 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.484 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.641 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.227 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.559 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.559 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.400 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.560 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.713 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.442 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.811 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.425 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.209 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.451 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.591 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.211 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.542 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.544 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.458 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.535 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.639 Epoch: [7] [ 0/40] eta: 0:00:49 lr: 0.005000 loss: 0.3007 (0.3007) loss_classifier: 0.0478 (0.0478) loss_box_reg: 0.1082 (0.1082) loss_mask: 0.1297 (0.1297) loss_objectness: 0.0041 (0.0041) loss_rpn_box_reg: 0.0109 (0.0109) time: 1.2296 data: 0.7325 max mem: 5189 Epoch: [7] [10/40] eta: 0:00:18 lr: 0.005000 loss: 0.2718 (0.2818) loss_classifier: 0.0468 (0.0424) loss_box_reg: 0.0843 (0.0876) loss_mask: 0.1297 (0.1391) loss_objectness: 0.0029 (0.0040) loss_rpn_box_reg: 0.0079 (0.0087) time: 0.6246 data: 0.0727 max mem: 5189 Epoch: [7] [20/40] eta: 0:00:12 lr: 0.005000 loss: 0.2933 (0.2964) loss_classifier: 0.0451 (0.0424) loss_box_reg: 0.0871 (0.0939) loss_mask: 0.1357 (0.1440) loss_objectness: 0.0020 (0.0037) loss_rpn_box_reg: 0.0079 (0.0123) time: 0.5807 data: 0.0073 max mem: 5190 Epoch: [7] [30/40] eta: 0:00:06 lr: 0.005000 loss: 0.3123 (0.3058) loss_classifier: 0.0451 (0.0444) loss_box_reg: 0.0973 (0.0996) loss_mask: 0.1452 (0.1466) loss_objectness: 0.0024 (0.0033) loss_rpn_box_reg: 0.0090 (0.0120) time: 0.6012 data: 0.0077 max mem: 5190 Epoch: [7] [39/40] eta: 0:00:00 lr: 0.005000 loss: 0.3041 (0.2994) loss_classifier: 0.0399 (0.0427) loss_box_reg: 0.0927 (0.0956) loss_mask: 0.1427 (0.1417) loss_objectness: 0.0016 (0.0031) loss_rpn_box_reg: 0.0042 (0.0163) time: 0.6019 data: 0.0077 max mem: 5190 Epoch: [7] Total time: 0:00:24 (0.6091 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:07 model_time: 0.2377 (0.2377) evaluator_time: 0.0292 (0.0292) time: 0.7844 data: 0.5143 max mem: 5190 Test: [ 9/10] eta: 0:00:00 model_time: 0.2071 (0.2105) evaluator_time: 0.0210 (0.0221) time: 0.2916 data: 0.0561 max mem: 5190 Test: Total time: 0:00:02 (0.2976 s / it) Averaged stats: model_time: 0.2071 (0.2105) evaluator_time: 0.0210 (0.0221) Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.485 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.867 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.465 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.284 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.490 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.650 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.228 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.570 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.573 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.400 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.580 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.704 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.454 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.835 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.460 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.215 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.453 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.647 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.215 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.552 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.553 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.447 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.544 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.665 Epoch: [8] [ 0/40] eta: 0:00:48 lr: 0.005000 loss: 0.2784 (0.2784) loss_classifier: 0.0362 (0.0362) loss_box_reg: 0.0856 (0.0856) loss_mask: 0.1511 (0.1511) loss_objectness: 0.0010 (0.0010) loss_rpn_box_reg: 0.0046 (0.0046) time: 1.2250 data: 0.7315 max mem: 5190 Epoch: [8] [10/40] eta: 0:00:18 lr: 0.005000 loss: 0.2815 (0.2693) loss_classifier: 0.0371 (0.0370) loss_box_reg: 0.0856 (0.0884) loss_mask: 0.1482 (0.1330) loss_objectness: 0.0015 (0.0021) loss_rpn_box_reg: 0.0054 (0.0088) time: 0.6326 data: 0.0733 max mem: 5190 Epoch: [8] [20/40] eta: 0:00:12 lr: 0.005000 loss: 0.2772 (0.2753) loss_classifier: 0.0384 (0.0394) loss_box_reg: 0.0864 (0.0893) loss_mask: 0.1252 (0.1322) loss_objectness: 0.0018 (0.0032) loss_rpn_box_reg: 0.0052 (0.0113) time: 0.5873 data: 0.0077 max mem: 5190 Epoch: [8] [30/40] eta: 0:00:06 lr: 0.005000 loss: 0.2750 (0.2873) loss_classifier: 0.0425 (0.0402) loss_box_reg: 0.0868 (0.0913) loss_mask: 0.1354 (0.1376) loss_objectness: 0.0020 (0.0028) loss_rpn_box_reg: 0.0052 (0.0153) time: 0.6024 data: 0.0078 max mem: 5190 Epoch: [8] [39/40] eta: 0:00:00 lr: 0.005000 loss: 0.2874 (0.2828) loss_classifier: 0.0406 (0.0399) loss_box_reg: 0.0969 (0.0902) loss_mask: 0.1381 (0.1350) loss_objectness: 0.0014 (0.0029) loss_rpn_box_reg: 0.0064 (0.0148) time: 0.6029 data: 0.0078 max mem: 5190 Epoch: [8] Total time: 0:00:24 (0.6124 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:07 model_time: 0.2368 (0.2368) evaluator_time: 0.0268 (0.0268) time: 0.7932 data: 0.5267 max mem: 5190 Test: [ 9/10] eta: 0:00:00 model_time: 0.2076 (0.2102) evaluator_time: 0.0200 (0.0213) time: 0.2919 data: 0.0577 max mem: 5190 Test: Total time: 0:00:02 (0.2977 s / it) Averaged stats: model_time: 0.2076 (0.2102) evaluator_time: 0.0200 (0.0213) Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.498 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.851 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.540 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.290 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.506 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.665 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.234 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.590 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.590 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.405 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.596 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.730 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.451 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.827 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.442 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.192 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.468 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.590 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.214 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.543 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.543 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.458 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.533 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.635 Epoch: [9] [ 0/40] eta: 0:00:49 lr: 0.005000 loss: 0.3162 (0.3162) loss_classifier: 0.0453 (0.0453) loss_box_reg: 0.1101 (0.1101) loss_mask: 0.1526 (0.1526) loss_objectness: 0.0014 (0.0014) loss_rpn_box_reg: 0.0068 (0.0068) time: 1.2318 data: 0.7385 max mem: 5190 Epoch: [9] [10/40] eta: 0:00:18 lr: 0.005000 loss: 0.2456 (0.2637) loss_classifier: 0.0376 (0.0348) loss_box_reg: 0.0793 (0.0794) loss_mask: 0.1217 (0.1348) loss_objectness: 0.0016 (0.0020) loss_rpn_box_reg: 0.0034 (0.0127) time: 0.6246 data: 0.0730 max mem: 5190 Epoch: [9] [20/40] eta: 0:00:12 lr: 0.005000 loss: 0.2418 (0.2679) loss_classifier: 0.0351 (0.0346) loss_box_reg: 0.0713 (0.0750) loss_mask: 0.1196 (0.1299) loss_objectness: 0.0017 (0.0020) loss_rpn_box_reg: 0.0049 (0.0264) time: 0.5792 data: 0.0071 max mem: 5190 Epoch: [9] [30/40] eta: 0:00:06 lr: 0.005000 loss: 0.2936 (0.2858) loss_classifier: 0.0425 (0.0390) loss_box_reg: 0.0975 (0.0865) loss_mask: 0.1332 (0.1365) loss_objectness: 0.0018 (0.0020) loss_rpn_box_reg: 0.0085 (0.0218) time: 0.6054 data: 0.0078 max mem: 5190 Epoch: [9] [39/40] eta: 0:00:00 lr: 0.005000 loss: 0.2970 (0.2839) loss_classifier: 0.0435 (0.0397) loss_box_reg: 0.0993 (0.0879) loss_mask: 0.1364 (0.1355) loss_objectness: 0.0016 (0.0021) loss_rpn_box_reg: 0.0075 (0.0188) time: 0.6129 data: 0.0078 max mem: 5190 Epoch: [9] Total time: 0:00:24 (0.6138 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:07 model_time: 0.2418 (0.2418) evaluator_time: 0.0310 (0.0310) time: 0.7868 data: 0.5110 max mem: 5190 Test: [ 9/10] eta: 0:00:00 model_time: 0.2108 (0.2142) evaluator_time: 0.0237 (0.0237) time: 0.2964 data: 0.0557 max mem: 5190 Test: Total time: 0:00:03 (0.3025 s / it) Averaged stats: model_time: 0.2108 (0.2142) evaluator_time: 0.0237 (0.0237) Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.509 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.846 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.527 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.305 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.512 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.680 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.235 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.590 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.590 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.432 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.588 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.730 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.462 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.815 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.456 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.192 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.464 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.637 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.219 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.547 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.547 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.437 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.540 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.652 Epoch: [10] [ 0/40] eta: 0:00:47 lr: 0.000500 loss: 0.3321 (0.3321) loss_classifier: 0.0440 (0.0440) loss_box_reg: 0.1080 (0.1080) loss_mask: 0.1587 (0.1587) loss_objectness: 0.0030 (0.0030) loss_rpn_box_reg: 0.0183 (0.0183) time: 1.1958 data: 0.7041 max mem: 5190 Epoch: [10] [10/40] eta: 0:00:18 lr: 0.000500 loss: 0.2306 (0.2487) loss_classifier: 0.0328 (0.0343) loss_box_reg: 0.0713 (0.0790) loss_mask: 0.1195 (0.1283) loss_objectness: 0.0017 (0.0018) loss_rpn_box_reg: 0.0040 (0.0053) time: 0.6297 data: 0.0708 max mem: 5190 Epoch: [10] [20/40] eta: 0:00:12 lr: 0.000500 loss: 0.2306 (0.2417) loss_classifier: 0.0318 (0.0344) loss_box_reg: 0.0674 (0.0744) loss_mask: 0.1125 (0.1233) loss_objectness: 0.0013 (0.0019) loss_rpn_box_reg: 0.0044 (0.0077) time: 0.5867 data: 0.0075 max mem: 5190 Epoch: [10] [30/40] eta: 0:00:06 lr: 0.000500 loss: 0.2412 (0.2437) loss_classifier: 0.0337 (0.0351) loss_box_reg: 0.0692 (0.0751) loss_mask: 0.1215 (0.1248) loss_objectness: 0.0013 (0.0017) loss_rpn_box_reg: 0.0065 (0.0071) time: 0.6076 data: 0.0075 max mem: 5190 Epoch: [10] [39/40] eta: 0:00:00 lr: 0.000500 loss: 0.2475 (0.2550) loss_classifier: 0.0365 (0.0367) loss_box_reg: 0.0773 (0.0779) loss_mask: 0.1252 (0.1270) loss_objectness: 0.0011 (0.0017) loss_rpn_box_reg: 0.0053 (0.0116) time: 0.6154 data: 0.0075 max mem: 5190 Epoch: [10] Total time: 0:00:24 (0.6175 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:07 model_time: 0.2341 (0.2341) evaluator_time: 0.0288 (0.0288) time: 0.7587 data: 0.4926 max mem: 5190 Test: [ 9/10] eta: 0:00:00 model_time: 0.2070 (0.2116) evaluator_time: 0.0228 (0.0232) time: 0.2912 data: 0.0535 max mem: 5190 Test: Total time: 0:00:02 (0.2968 s / it) Averaged stats: model_time: 0.2070 (0.2116) evaluator_time: 0.0228 (0.0232) Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.508 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.844 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.523 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.314 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.514 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.668 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.229 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.597 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.597 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.484 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.587 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.739 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.455 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.839 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.444 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.182 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.464 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.618 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.215 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.547 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.549 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.458 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.533 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.665 Epoch: [11] [ 0/40] eta: 0:00:46 lr: 0.000500 loss: 0.2035 (0.2035) loss_classifier: 0.0314 (0.0314) loss_box_reg: 0.0525 (0.0525) loss_mask: 0.1153 (0.1153) loss_objectness: 0.0004 (0.0004) loss_rpn_box_reg: 0.0038 (0.0038) time: 1.1690 data: 0.6649 max mem: 5190 Epoch: [11] [10/40] eta: 0:00:18 lr: 0.000500 loss: 0.2174 (0.2344) loss_classifier: 0.0302 (0.0337) loss_box_reg: 0.0555 (0.0690) loss_mask: 0.1240 (0.1247) loss_objectness: 0.0009 (0.0011) loss_rpn_box_reg: 0.0038 (0.0059) time: 0.6311 data: 0.0677 max mem: 5190 Epoch: [11] [20/40] eta: 0:00:12 lr: 0.000500 loss: 0.2174 (0.2293) loss_classifier: 0.0291 (0.0326) loss_box_reg: 0.0588 (0.0660) loss_mask: 0.1240 (0.1237) loss_objectness: 0.0008 (0.0011) loss_rpn_box_reg: 0.0038 (0.0059) time: 0.5906 data: 0.0080 max mem: 5190 Epoch: [11] [30/40] eta: 0:00:06 lr: 0.000500 loss: 0.2337 (0.2476) loss_classifier: 0.0312 (0.0350) loss_box_reg: 0.0716 (0.0697) loss_mask: 0.1262 (0.1259) loss_objectness: 0.0008 (0.0019) loss_rpn_box_reg: 0.0041 (0.0151) time: 0.6069 data: 0.0079 max mem: 5190 Epoch: [11] [39/40] eta: 0:00:00 lr: 0.000500 loss: 0.2337 (0.2495) loss_classifier: 0.0383 (0.0353) loss_box_reg: 0.0788 (0.0721) loss_mask: 0.1262 (0.1265) loss_objectness: 0.0013 (0.0022) loss_rpn_box_reg: 0.0043 (0.0135) time: 0.6157 data: 0.0077 max mem: 5190 Epoch: [11] Total time: 0:00:24 (0.6188 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:07 model_time: 0.2466 (0.2466) evaluator_time: 0.0265 (0.0265) time: 0.7916 data: 0.5153 max mem: 5190 Test: [ 9/10] eta: 0:00:00 model_time: 0.2073 (0.2125) evaluator_time: 0.0216 (0.0228) time: 0.2950 data: 0.0568 max mem: 5190 Test: Total time: 0:00:03 (0.3009 s / it) Averaged stats: model_time: 0.2073 (0.2125) evaluator_time: 0.0216 (0.0228) Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.506 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.846 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.527 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.299 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.518 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.638 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.234 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.586 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.586 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.447 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.589 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.700 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.457 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.828 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.444 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.182 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.465 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.625 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.214 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.553 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.553 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.463 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.539 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.665 Epoch: [12] [ 0/40] eta: 0:00:51 lr: 0.000500 loss: 0.3570 (0.3570) loss_classifier: 0.0595 (0.0595) loss_box_reg: 0.1149 (0.1149) loss_mask: 0.1768 (0.1768) loss_objectness: 0.0016 (0.0016) loss_rpn_box_reg: 0.0042 (0.0042) time: 1.2779 data: 0.7711 max mem: 5190 Epoch: [12] [10/40] eta: 0:00:19 lr: 0.000500 loss: 0.2179 (0.2348) loss_classifier: 0.0281 (0.0352) loss_box_reg: 0.0571 (0.0694) loss_mask: 0.1146 (0.1191) loss_objectness: 0.0016 (0.0021) loss_rpn_box_reg: 0.0075 (0.0090) time: 0.6373 data: 0.0754 max mem: 5190 Epoch: [12] [20/40] eta: 0:00:12 lr: 0.000500 loss: 0.2297 (0.2334) loss_classifier: 0.0281 (0.0334) loss_box_reg: 0.0571 (0.0658) loss_mask: 0.1186 (0.1254) loss_objectness: 0.0012 (0.0017) loss_rpn_box_reg: 0.0053 (0.0069) time: 0.5885 data: 0.0068 max mem: 5190 Epoch: [12] [30/40] eta: 0:00:06 lr: 0.000500 loss: 0.2497 (0.2481) loss_classifier: 0.0351 (0.0349) loss_box_reg: 0.0658 (0.0693) loss_mask: 0.1264 (0.1265) loss_objectness: 0.0011 (0.0020) loss_rpn_box_reg: 0.0032 (0.0153) time: 0.6101 data: 0.0078 max mem: 5190 Epoch: [12] [39/40] eta: 0:00:00 lr: 0.000500 loss: 0.2469 (0.2523) loss_classifier: 0.0351 (0.0351) loss_box_reg: 0.0689 (0.0732) loss_mask: 0.1258 (0.1284) loss_objectness: 0.0015 (0.0021) loss_rpn_box_reg: 0.0035 (0.0135) time: 0.6176 data: 0.0077 max mem: 5190 Epoch: [12] Total time: 0:00:24 (0.6217 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:07 model_time: 0.2384 (0.2384) evaluator_time: 0.0313 (0.0313) time: 0.7826 data: 0.5098 max mem: 5190 Test: [ 9/10] eta: 0:00:00 model_time: 0.2071 (0.2114) evaluator_time: 0.0212 (0.0228) time: 0.2926 data: 0.0556 max mem: 5190 Test: Total time: 0:00:02 (0.2985 s / it) Averaged stats: model_time: 0.2071 (0.2114) evaluator_time: 0.0212 (0.0228) Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.511 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.839 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.523 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.298 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.526 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.640 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.245 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.590 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.590 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.442 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.595 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.704 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.460 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.828 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.450 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.187 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.474 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.603 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.212 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.550 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.550 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.458 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.540 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.648 Epoch: [13] [ 0/40] eta: 0:00:46 lr: 0.000500 loss: 0.2275 (0.2275) loss_classifier: 0.0289 (0.0289) loss_box_reg: 0.0634 (0.0634) loss_mask: 0.1276 (0.1276) loss_objectness: 0.0006 (0.0006) loss_rpn_box_reg: 0.0071 (0.0071) time: 1.1639 data: 0.6723 max mem: 5190 Epoch: [13] [10/40] eta: 0:00:19 lr: 0.000500 loss: 0.2275 (0.2309) loss_classifier: 0.0289 (0.0288) loss_box_reg: 0.0691 (0.0649) loss_mask: 0.1231 (0.1292) loss_objectness: 0.0014 (0.0024) loss_rpn_box_reg: 0.0051 (0.0056) time: 0.6364 data: 0.0684 max mem: 5190 Epoch: [13] [20/40] eta: 0:00:12 lr: 0.000500 loss: 0.2297 (0.2264) loss_classifier: 0.0304 (0.0313) loss_box_reg: 0.0692 (0.0662) loss_mask: 0.1195 (0.1214) loss_objectness: 0.0009 (0.0021) loss_rpn_box_reg: 0.0042 (0.0054) time: 0.5977 data: 0.0079 max mem: 5190 Epoch: [13] [30/40] eta: 0:00:06 lr: 0.000500 loss: 0.2516 (0.2475) loss_classifier: 0.0347 (0.0338) loss_box_reg: 0.0748 (0.0712) loss_mask: 0.1243 (0.1254) loss_objectness: 0.0014 (0.0024) loss_rpn_box_reg: 0.0042 (0.0147) time: 0.6137 data: 0.0078 max mem: 5190 Epoch: [13] [39/40] eta: 0:00:00 lr: 0.000500 loss: 0.2674 (0.2471) loss_classifier: 0.0375 (0.0338) loss_box_reg: 0.0769 (0.0716) loss_mask: 0.1261 (0.1266) loss_objectness: 0.0019 (0.0022) loss_rpn_box_reg: 0.0061 (0.0129) time: 0.6152 data: 0.0077 max mem: 5190 Epoch: [13] Total time: 0:00:24 (0.6218 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:07 model_time: 0.2262 (0.2262) evaluator_time: 0.0280 (0.0280) time: 0.7557 data: 0.4983 max mem: 5190 Test: [ 9/10] eta: 0:00:00 model_time: 0.2076 (0.2100) evaluator_time: 0.0208 (0.0223) time: 0.2903 data: 0.0550 max mem: 5190 Test: Total time: 0:00:02 (0.2967 s / it) Averaged stats: model_time: 0.2076 (0.2100) evaluator_time: 0.0208 (0.0223) Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.503 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.848 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.530 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.292 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.522 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.636 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.239 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.581 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.581 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.416 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.592 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.700 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.471 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.842 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.469 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.181 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.488 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.624 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.558 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.558 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.432 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.551 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.674 Epoch: [14] [ 0/40] eta: 0:00:47 lr: 0.000500 loss: 0.1741 (0.1741) loss_classifier: 0.0218 (0.0218) loss_box_reg: 0.0544 (0.0544) loss_mask: 0.0942 (0.0942) loss_objectness: 0.0019 (0.0019) loss_rpn_box_reg: 0.0018 (0.0018) time: 1.1930 data: 0.6933 max mem: 5190 Epoch: [14] [10/40] eta: 0:00:19 lr: 0.000500 loss: 0.2146 (0.2241) loss_classifier: 0.0331 (0.0341) loss_box_reg: 0.0594 (0.0659) loss_mask: 0.1116 (0.1187) loss_objectness: 0.0015 (0.0014) loss_rpn_box_reg: 0.0023 (0.0040) time: 0.6368 data: 0.0699 max mem: 5190 Epoch: [14] [20/40] eta: 0:00:12 lr: 0.000500 loss: 0.2088 (0.2202) loss_classifier: 0.0298 (0.0316) loss_box_reg: 0.0577 (0.0618) loss_mask: 0.1152 (0.1212) loss_objectness: 0.0006 (0.0013) loss_rpn_box_reg: 0.0025 (0.0043) time: 0.5918 data: 0.0075 max mem: 5190 Epoch: [14] [30/40] eta: 0:00:06 lr: 0.000500 loss: 0.2371 (0.2380) loss_classifier: 0.0306 (0.0334) loss_box_reg: 0.0566 (0.0666) loss_mask: 0.1207 (0.1230) loss_objectness: 0.0009 (0.0018) loss_rpn_box_reg: 0.0047 (0.0133) time: 0.6113 data: 0.0074 max mem: 5190 Epoch: [14] [39/40] eta: 0:00:00 lr: 0.000500 loss: 0.2501 (0.2438) loss_classifier: 0.0373 (0.0353) loss_box_reg: 0.0720 (0.0685) loss_mask: 0.1260 (0.1259) loss_objectness: 0.0011 (0.0019) loss_rpn_box_reg: 0.0070 (0.0123) time: 0.6194 data: 0.0072 max mem: 5190 Epoch: [14] Total time: 0:00:24 (0.6222 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:07 model_time: 0.2269 (0.2269) evaluator_time: 0.0232 (0.0232) time: 0.7710 data: 0.5176 max mem: 5190 Test: [ 9/10] eta: 0:00:00 model_time: 0.2077 (0.2095) evaluator_time: 0.0206 (0.0210) time: 0.2900 data: 0.0566 max mem: 5190 Test: Total time: 0:00:02 (0.2958 s / it) Averaged stats: model_time: 0.2077 (0.2095) evaluator_time: 0.0206 (0.0210) Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.495 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.839 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.506 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.292 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.513 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.619 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.243 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.572 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.572 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.416 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.579 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.687 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.453 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.829 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.441 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.174 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.468 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.620 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.213 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.544 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.544 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.432 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.529 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.674 Epoch: [15] [ 0/40] eta: 0:00:48 lr: 0.000500 loss: 0.2820 (0.2820) loss_classifier: 0.0364 (0.0364) loss_box_reg: 0.0847 (0.0847) loss_mask: 0.1516 (0.1516) loss_objectness: 0.0037 (0.0037) loss_rpn_box_reg: 0.0055 (0.0055) time: 1.2072 data: 0.7219 max mem: 5190 Epoch: [15] [10/40] eta: 0:00:19 lr: 0.000500 loss: 0.2736 (0.2588) loss_classifier: 0.0356 (0.0388) loss_box_reg: 0.0792 (0.0782) loss_mask: 0.1396 (0.1347) loss_objectness: 0.0016 (0.0020) loss_rpn_box_reg: 0.0034 (0.0051) time: 0.6397 data: 0.0724 max mem: 5190 Epoch: [15] [20/40] eta: 0:00:12 lr: 0.000500 loss: 0.2405 (0.2455) loss_classifier: 0.0326 (0.0358) loss_box_reg: 0.0674 (0.0752) loss_mask: 0.1295 (0.1283) loss_objectness: 0.0005 (0.0013) loss_rpn_box_reg: 0.0034 (0.0049) time: 0.5945 data: 0.0073 max mem: 5190 Epoch: [15] [30/40] eta: 0:00:06 lr: 0.000500 loss: 0.2194 (0.2326) loss_classifier: 0.0254 (0.0329) loss_box_reg: 0.0592 (0.0688) loss_mask: 0.1149 (0.1242) loss_objectness: 0.0008 (0.0015) loss_rpn_box_reg: 0.0043 (0.0053) time: 0.6093 data: 0.0073 max mem: 5190 Epoch: [15] [39/40] eta: 0:00:00 lr: 0.000500 loss: 0.2208 (0.2349) loss_classifier: 0.0311 (0.0329) loss_box_reg: 0.0620 (0.0678) loss_mask: 0.1149 (0.1229) loss_objectness: 0.0008 (0.0016) loss_rpn_box_reg: 0.0054 (0.0097) time: 0.6177 data: 0.0075 max mem: 5190 Epoch: [15] Total time: 0:00:24 (0.6226 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:07 model_time: 0.2261 (0.2261) evaluator_time: 0.0241 (0.0241) time: 0.7789 data: 0.5254 max mem: 5190 Test: [ 9/10] eta: 0:00:00 model_time: 0.2057 (0.2090) evaluator_time: 0.0199 (0.0207) time: 0.2904 data: 0.0577 max mem: 5190 Test: Total time: 0:00:02 (0.2961 s / it) Averaged stats: model_time: 0.2057 (0.2090) evaluator_time: 0.0199 (0.0207) Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.507 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.846 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.525 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.295 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.530 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.620 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.245 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.578 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.580 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.405 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.597 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.683 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.462 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.837 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.467 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.181 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.480 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.615 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.210 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.547 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.547 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.416 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.543 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.657 Epoch: [16] [ 0/40] eta: 0:00:47 lr: 0.000500 loss: 0.3187 (0.3187) loss_classifier: 0.0470 (0.0470) loss_box_reg: 0.0923 (0.0923) loss_mask: 0.1577 (0.1577) loss_objectness: 0.0040 (0.0040) loss_rpn_box_reg: 0.0177 (0.0177) time: 1.1972 data: 0.6992 max mem: 5190 Epoch: [16] [10/40] eta: 0:00:19 lr: 0.000500 loss: 0.2466 (0.2551) loss_classifier: 0.0432 (0.0379) loss_box_reg: 0.0893 (0.0785) loss_mask: 0.1319 (0.1307) loss_objectness: 0.0012 (0.0014) loss_rpn_box_reg: 0.0054 (0.0066) time: 0.6415 data: 0.0705 max mem: 5190 Epoch: [16] [20/40] eta: 0:00:12 lr: 0.000500 loss: 0.2057 (0.2344) loss_classifier: 0.0285 (0.0338) loss_box_reg: 0.0562 (0.0657) loss_mask: 0.1138 (0.1213) loss_objectness: 0.0012 (0.0016) loss_rpn_box_reg: 0.0048 (0.0121) time: 0.5997 data: 0.0077 max mem: 5190 Epoch: [16] [30/40] eta: 0:00:06 lr: 0.000500 loss: 0.1889 (0.2253) loss_classifier: 0.0277 (0.0319) loss_box_reg: 0.0488 (0.0626) loss_mask: 0.1138 (0.1199) loss_objectness: 0.0010 (0.0014) loss_rpn_box_reg: 0.0030 (0.0095) time: 0.6131 data: 0.0078 max mem: 5190 Epoch: [16] [39/40] eta: 0:00:00 lr: 0.000500 loss: 0.2187 (0.2350) loss_classifier: 0.0299 (0.0335) loss_box_reg: 0.0605 (0.0671) loss_mask: 0.1178 (0.1235) loss_objectness: 0.0010 (0.0016) loss_rpn_box_reg: 0.0035 (0.0093) time: 0.6179 data: 0.0078 max mem: 5190 Epoch: [16] Total time: 0:00:25 (0.6252 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:07 model_time: 0.2340 (0.2340) evaluator_time: 0.0250 (0.0250) time: 0.7698 data: 0.5075 max mem: 5190 Test: [ 9/10] eta: 0:00:00 model_time: 0.2057 (0.2088) evaluator_time: 0.0195 (0.0201) time: 0.2875 data: 0.0556 max mem: 5190 Test: Total time: 0:00:02 (0.2937 s / it) Averaged stats: model_time: 0.2057 (0.2088) evaluator_time: 0.0195 (0.0201) Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.501 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.849 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.505 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.309 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.519 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.632 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.240 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.580 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.580 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.421 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.587 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.700 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.458 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.827 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.465 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.202 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.473 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.603 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.209 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.542 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.542 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.453 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.531 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.643 Epoch: [17] [ 0/40] eta: 0:00:47 lr: 0.000500 loss: 0.2313 (0.2313) loss_classifier: 0.0295 (0.0295) loss_box_reg: 0.0676 (0.0676) loss_mask: 0.1281 (0.1281) loss_objectness: 0.0005 (0.0005) loss_rpn_box_reg: 0.0057 (0.0057) time: 1.1759 data: 0.6829 max mem: 5190 Epoch: [17] [10/40] eta: 0:00:19 lr: 0.000500 loss: 0.2311 (0.2677) loss_classifier: 0.0329 (0.0356) loss_box_reg: 0.0644 (0.0722) loss_mask: 0.1313 (0.1307) loss_objectness: 0.0014 (0.0025) loss_rpn_box_reg: 0.0032 (0.0269) time: 0.6392 data: 0.0721 max mem: 5190 Epoch: [17] [20/40] eta: 0:00:12 lr: 0.000500 loss: 0.2159 (0.2499) loss_classifier: 0.0317 (0.0340) loss_box_reg: 0.0625 (0.0704) loss_mask: 0.1313 (0.1264) loss_objectness: 0.0008 (0.0019) loss_rpn_box_reg: 0.0032 (0.0171) time: 0.5984 data: 0.0092 max mem: 5190 Epoch: [17] [30/40] eta: 0:00:06 lr: 0.000500 loss: 0.2114 (0.2363) loss_classifier: 0.0304 (0.0333) loss_box_reg: 0.0577 (0.0664) loss_mask: 0.1172 (0.1223) loss_objectness: 0.0006 (0.0016) loss_rpn_box_reg: 0.0037 (0.0127) time: 0.6168 data: 0.0076 max mem: 5190 Epoch: [17] [39/40] eta: 0:00:00 lr: 0.000500 loss: 0.1931 (0.2321) loss_classifier: 0.0316 (0.0327) loss_box_reg: 0.0566 (0.0649) loss_mask: 0.1115 (0.1215) loss_objectness: 0.0006 (0.0016) loss_rpn_box_reg: 0.0037 (0.0114) time: 0.6197 data: 0.0078 max mem: 5190 Epoch: [17] Total time: 0:00:24 (0.6247 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:07 model_time: 0.2365 (0.2365) evaluator_time: 0.0254 (0.0254) time: 0.7710 data: 0.5060 max mem: 5190 Test: [ 9/10] eta: 0:00:00 model_time: 0.2052 (0.2091) evaluator_time: 0.0190 (0.0194) time: 0.2871 data: 0.0557 max mem: 5190 Test: Total time: 0:00:02 (0.2938 s / it) Averaged stats: model_time: 0.2052 (0.2091) evaluator_time: 0.0190 (0.0194) Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.503 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.843 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.510 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.302 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.519 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.638 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.228 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.581 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.581 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.421 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.589 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.700 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.459 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.832 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.456 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.189 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.478 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.600 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.212 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.542 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.542 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.432 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.536 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.643 Epoch: [18] [ 0/40] eta: 0:00:49 lr: 0.000500 loss: 0.1389 (0.1389) loss_classifier: 0.0246 (0.0246) loss_box_reg: 0.0311 (0.0311) loss_mask: 0.0781 (0.0781) loss_objectness: 0.0010 (0.0010) loss_rpn_box_reg: 0.0040 (0.0040) time: 1.2488 data: 0.7533 max mem: 5190 Epoch: [18] [10/40] eta: 0:00:19 lr: 0.000500 loss: 0.2122 (0.2432) loss_classifier: 0.0293 (0.0333) loss_box_reg: 0.0531 (0.0733) loss_mask: 0.1174 (0.1266) loss_objectness: 0.0021 (0.0023) loss_rpn_box_reg: 0.0070 (0.0077) time: 0.6452 data: 0.0741 max mem: 5190 Epoch: [18] [20/40] eta: 0:00:12 lr: 0.000500 loss: 0.2122 (0.2323) loss_classifier: 0.0280 (0.0325) loss_box_reg: 0.0610 (0.0694) loss_mask: 0.1146 (0.1224) loss_objectness: 0.0010 (0.0017) loss_rpn_box_reg: 0.0029 (0.0062) time: 0.5998 data: 0.0067 max mem: 5190 Epoch: [18] [30/40] eta: 0:00:06 lr: 0.000500 loss: 0.2194 (0.2341) loss_classifier: 0.0324 (0.0323) loss_box_reg: 0.0615 (0.0663) loss_mask: 0.1136 (0.1208) loss_objectness: 0.0006 (0.0016) loss_rpn_box_reg: 0.0028 (0.0132) time: 0.6141 data: 0.0074 max mem: 5190 Epoch: [18] [39/40] eta: 0:00:00 lr: 0.000500 loss: 0.2194 (0.2331) loss_classifier: 0.0324 (0.0326) loss_box_reg: 0.0604 (0.0653) loss_mask: 0.1180 (0.1226) loss_objectness: 0.0007 (0.0015) loss_rpn_box_reg: 0.0041 (0.0112) time: 0.6128 data: 0.0076 max mem: 5190 Epoch: [18] Total time: 0:00:24 (0.6241 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:07 model_time: 0.2258 (0.2258) evaluator_time: 0.0291 (0.0291) time: 0.7811 data: 0.5229 max mem: 5190 Test: [ 9/10] eta: 0:00:00 model_time: 0.2061 (0.2082) evaluator_time: 0.0199 (0.0207) time: 0.2885 data: 0.0567 max mem: 5190 Test: Total time: 0:00:02 (0.2941 s / it) Averaged stats: model_time: 0.2061 (0.2082) evaluator_time: 0.0199 (0.0207) Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.512 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.840 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.526 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.310 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.523 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.669 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.239 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.584 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.584 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.416 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.588 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.730 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.462 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.834 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.470 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.198 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.469 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.608 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.215 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.546 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.546 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.421 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.541 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.652 Epoch: [19] [ 0/40] eta: 0:00:50 lr: 0.000500 loss: 0.1895 (0.1895) loss_classifier: 0.0305 (0.0305) loss_box_reg: 0.0564 (0.0564) loss_mask: 0.0994 (0.0994) loss_objectness: 0.0007 (0.0007) loss_rpn_box_reg: 0.0025 (0.0025) time: 1.2744 data: 0.7670 max mem: 5190 Epoch: [19] [10/40] eta: 0:00:19 lr: 0.000500 loss: 0.1949 (0.2342) loss_classifier: 0.0300 (0.0327) loss_box_reg: 0.0547 (0.0607) loss_mask: 0.1105 (0.1160) loss_objectness: 0.0007 (0.0015) loss_rpn_box_reg: 0.0025 (0.0233) time: 0.6384 data: 0.0755 max mem: 5190 Epoch: [19] [20/40] eta: 0:00:12 lr: 0.000500 loss: 0.2128 (0.2312) loss_classifier: 0.0313 (0.0329) loss_box_reg: 0.0546 (0.0607) loss_mask: 0.1210 (0.1211) loss_objectness: 0.0010 (0.0014) loss_rpn_box_reg: 0.0038 (0.0150) time: 0.5912 data: 0.0070 max mem: 5190 Epoch: [19] [30/40] eta: 0:00:06 lr: 0.000500 loss: 0.2133 (0.2261) loss_classifier: 0.0282 (0.0313) loss_box_reg: 0.0546 (0.0613) loss_mask: 0.1152 (0.1204) loss_objectness: 0.0011 (0.0013) loss_rpn_box_reg: 0.0038 (0.0119) time: 0.6125 data: 0.0074 max mem: 5190 Epoch: [19] [39/40] eta: 0:00:00 lr: 0.000500 loss: 0.2221 (0.2296) loss_classifier: 0.0327 (0.0329) loss_box_reg: 0.0637 (0.0645) loss_mask: 0.1152 (0.1204) loss_objectness: 0.0008 (0.0012) loss_rpn_box_reg: 0.0044 (0.0107) time: 0.6198 data: 0.0074 max mem: 5190 Epoch: [19] Total time: 0:00:24 (0.6238 s / it) creating index... index created! Test: [ 0/10] eta: 0:00:07 model_time: 0.2336 (0.2336) evaluator_time: 0.0330 (0.0330) time: 0.7830 data: 0.5135 max mem: 5190 Test: [ 9/10] eta: 0:00:00 model_time: 0.2039 (0.2076) evaluator_time: 0.0177 (0.0197) time: 0.2866 data: 0.0564 max mem: 5190 Test: Total time: 0:00:02 (0.2935 s / it) Averaged stats: model_time: 0.2039 (0.2076) evaluator_time: 0.0177 (0.0197) Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.499 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.845 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.505 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.300 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.521 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.620 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.234 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.574 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.574 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.405 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.591 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.674 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.450 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.811 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.463 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.188 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.466 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.593 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.206 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.541 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.541 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.432 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.535 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.643
# pick one image from the test set
img, _ = data_loader_test.dataset[0]
# put the model in evaluation mode
model.eval()
with torch.no_grad():
prediction = model([img.to(device)])
prediction
[{'boxes': tensor([[296.2079, 193.3952, 453.0812, 267.1093],
[442.2224, 187.0209, 616.6365, 252.8787],
[295.5228, 212.7988, 341.5661, 258.0097],
[295.8234, 197.4667, 370.7387, 262.2480],
[369.6344, 216.4189, 417.1077, 230.0708],
[326.0764, 192.2509, 429.9577, 242.1528]], device='cuda:0'),
'labels': tensor([1, 1, 1, 1, 1, 1], device='cuda:0'),
'scores': tensor([0.9996, 0.9995, 0.9818, 0.2625, 0.2471, 0.2113], device='cuda:0'),
'masks': tensor([[[[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]]],
[[[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]]],
[[[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]]],
[[[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]]],
[[[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]]],
[[[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]]]], device='cuda:0')}]
from dolphins_recognition_challenge.datasets import stack_imgs
def show_pred(dl, n=None, score_limit=0.5, width=600):
dataset_test = dl.dataset
if n == None:
n = len(dataset_test)
for i in range(n):
img = dataset_test[i][0]
img_bg = Image.fromarray(img.mul(255).permute(1, 2, 0).byte().numpy())
images = [img_bg]
model.eval()
with torch.no_grad():
prediction = model([img.to(device)])
predicted_masks = prediction[0]["masks"]
scores = prediction[0]["scores"]
for i in range(predicted_masks.shape[0]):
score = scores[i]
if score >= score_limit:
bg = img_bg.copy()
fg = Image.fromarray(predicted_masks[i, 0].mul(255).byte().cpu().numpy())
bg.paste(fg.convert("RGB"), (0, 0), fg)
images.append(bg)
display(stack_imgs(images, width))
show_pred(data_loader_test, score_limit=0.5, width=1200)
def _save_model_with_timestamp(
model, save_path="/work/data/dupini/processed/body_100_resized/"
):
save_date_path = (
save_path + "model" + datetime.now().strftime("-%Y-%m-%d-%H-%M-%S") + ".pt"
)
print(save_date_path)
torch.save(model.state_dict(), save_date_path)